2 citations · 2 across the 5 of their papers we have counts for
5 papers
DALPHIN: Benchmarking Digital Pathology AI Copilots Against Pathologists on an Open Multicentric Dataset
Carlijn Lems, Sander Moonemans, Natálie Klubíčková +53
Foundation models with visual question answering capabilities for digital pathology are emerging. Such unprecedented technology requires independent benchmarking to assess its pote…
OCELOT 2023: Cell Detection from Cell-Tissue Interaction Challenge
JaeWoong Shin, Jeongun Ryu, Aaron Valero Puche +21
Pathologists routinely alternate between different magnifications when examining Whole-Slide Images, allowing them to evaluate both broad tissue morphology and intricate cellular d…
Identifying actionable driver mutations in lung cancer using an efficient Asymmetric Transformer Decoder
Biagio Brattoli, Jack Shi, Jongchan Park +3
Identifying actionable driver mutations in non-small cell lung cancer (NSCLC) can impact treatment decisions and significantly improve patient outcomes. Despite guideline recommend…
Pathology Foundation Models are Scanner Sensitive: Benchmark and Mitigation with Contrastive ScanGen Loss
Gianluca Carloni, Biagio Brattoli, Seongho Keum +4
Computational pathology (CPath) has shown great potential in mining actionable insights from Whole Slide Images (WSIs). Deep Learning (DL) has been at the center of modern CPath, a…
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Jeongun Ryu, Aaron Valero Puche, JaeWoong Shin +9
Cell detection is a fundamental task in computational pathology that can be used for extracting high-level medical information from whole-slide images. For accurate cell detection,…